GEOapplication/xmlftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE342nnn/GSE342306/primaryOK200GenomicsHomo sapiensNon-coding RNA profiling by high throughput sequencinghttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE342306GEOGSEfalseOM-sqPCR enables one-Step multiplex quantification of circulating small-RNA subclasses for colorectal cancer classificationAccurate and multiplexed quantification of circulating small RNAs (sRNAs) remains technically challenging due to their structural diversity and low abundance. Here we present OM-sqPCR, an AI-assisted one-step multiplex quantitative PCR platform that enables simultaneous detection of microRNAs, transfer RNA–derived sRNAs (tsRNAs), and ribosomal RNA–derived sRNAs (rsRNAs) in a single closed-tube reaction. The system integrates optimized enzyme–buffer chemistry for short RNA templates with machine-learning–guided primer–probe design, enabling synchronized amplification across multiple optical channels. Applied to a multicentre clinical cohort (n = 651), OM-sqPCR identified a four-sRNA signature that discriminated colorectal cancer from controls with an area under the ROC curve (AUC) = 0.97, 85.9% sensitivity, and 93.1% specificity, outperforming serum carcinoembryonic antigen (CEA) and maintaining high accuracy in early-stage disease (stage I–II, sensitivity: 84.6%). The workflow also validated biomarker panels for renal and thyroid cancers with comparable accuracy (AUC > 0.98). This generalizable, low-cost platform provides a scalable and automation-ready solution for unified small-RNA quantification across diverse clinical contexts.2026/08/12GSE342306GSM9927698GSM9927709GSM9927707GSM9927708GSM9927712GSM9927701GSM9927702GSM9927713GSM9927710GSM9927699GSM9927700GSM9927711GSM9927705GSM9927716GSM9927706GSM9927717GSM9927703GSM9927714GSM9927715GSM992770424676342306Homo sapiens